An Indoor Collaborative Pedestrian Dead Reckoning System

Li, Yiting; Chen, Guaning; Sun, Min-Te

doi:10.1109/icpp.2013.110

Cited by 11 publications

(6 citation statements)

References 20 publications

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“…A majority of research focuses on positioning scenarios in existing buildings, some of them re-using already existing infrastructure (i.e., signals of opportunity). Typical scenarios that are receiving a growing interest involve locating people within houses, offices [ 63 , 115 , 123 , 131 ], and universities [ 40 , 43 , 63 , 79 , 98 , 101 , 119 , 120 , 128 , 131 ], where the viability of installing complex infrastructure is low in terms of costs, unlike industrial or warehouse scenarios, which are capable of developing and deploying robust and costly infrastructures designed for positioning. As a result, our review indeed shows that infrastructure-less approaches are predominantly selected for CIPSs in research (see Figure 4 c), and that the majority of technologies used (see Figure 5 and Figure 6 ) are already present in the environment.…”

Section: Discussionmentioning

confidence: 99%

“…Regarding stand-alone positioning in the non-collaborative phase, the most used methods were PDR [ 43 , 63 , 92 , 97 , 98 , 100 , 101 , 102 , 106 , 107 , 110 , 117 , 118 , 120 , 123 , 127 , 128 , 129 , 131 , 141 , 144 , 145 , 146 , 147 ], Ranging [ 42 , 46 , 47 , 81 , 93 , 109 , 111 , 113 , 130 , 132 , 144 , 150 ], RSS-based [ 40 , 41 , 43 , 48 , 77 , 84 , 89 , 103 , 123 , 129 ], k -NN [ 63 , 79 , 108 , 114 , 118 , 119 ,…”

Section: Discussionmentioning

confidence: 99%

“…The study selection process, with step-wise results, is schematically depicted using the PRISMA flow diagram in Figure 2 . As a final set of eligible studies, 84 articles [ 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 63 , 77 , 79 , 80 , 81 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 ,…”

Section: Methodsmentioning

confidence: 99%

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Collaborative Indoor Positioning Systems: A Systematic Review

Pascacio

Casteleyn

Torres-Sospedra

et al. 2021

Sensors

101

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Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts. In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in (collaborative) indoor positioning systems a large variety of technologies, techniques, and methods is being used. Moreover, the diversity of evaluation procedures and scenarios hinders a direct comparison. This paper presents a systematic review that gives a general view of the current CIPSs. A total of 84 works, published between 2006 and 2020, have been identified. These articles were analyzed and classified according to the described system’s architecture, infrastructure, technologies, techniques, methods, and evaluation. The results indicate a growing interest in collaborative positioning, and the trend tend to be towards the use of distributed architectures and infrastructure-less systems. Moreover, the most used technologies to determine the collaborative positioning between users are wireless communication technologies (Wi-Fi, Ultra-WideBand, and Bluetooth). The predominant collaborative positioning techniques are Received Signal Strength Indication, Fingerprinting, and Time of Arrival/Flight, and the collaborative methods are particle filters, Belief Propagation, Extended Kalman Filter, and Least Squares. Simulations are used as the main evaluation procedure. On the basis of the analysis and results, several promising future research avenues and gaps in research were identified.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Collaborative Indoor Positioning Systems: A Systematic Review

Pascacio

Casteleyn

Torres-Sospedra

et al. 2021

Sensors

101

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“…1) is to estimate the pedestrian's heading. Heading can be obtained from either the magnetometer or gyroscope [24]. The magnetometer measures Earth's magnetic field.…”

Section: Heading Estimationmentioning

confidence: 99%

Low cost infrastructure free form of indoor positioning

Gupta

Box

Wilson

2014

2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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This paper is concerned with smartphone based inertial sensors for locating pedestrians. Two set of experiments were conducted over seven subjects. One set of the subjects walked along a straight path at a perceived normal and speedy pace, and the other set of subjects traversed a simple map consisting of two L shaped paths. In the experiments, a smartphone -HTC 2710e was handheld, with screen facing upwards was used to record the acceleration, magnetometer and gyroscope samples at 20Hz. The accelerometer samples are then analyzed to detect and count the footsteps, while gyroscope and magnetometer samples are used to estimate the heading. The results of our experiment show that the average error in foot step detection rate is less than 3% in normal walking and 5% in speedy walking using Fast Fourier Transform (FFT) based approach. While the most nearly correct estimated displacement is via Weiner approach with average percentage error of 5.74% in normal walking and 8.07% in speedy walking. The average percentage error in heading is less than 11 0 in turnings in both the L shaped paths.

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“…These two kinds of signal metrics were fused by the Kalman filter, and the the accuracy was improved. Li et al [105] proposed a similar PDR-based cooperative localization approach, in which PDR was used for self-tracking and acoustic ranging technique was used to detect proximity. When the proximity of two mobile targets was detected, which meant that their current locations would be equal, the PDR tracking results of two targets then were calibrated.…”

Section: Bayesian Estimationmentioning

confidence: 99%

Improvement Schemes for Indoor Mobile Location Estimation: A Survey

Shang

et al. 2015

Mathematical Problems in Engineering

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Location estimation is significant in mobile and ubiquitous computing systems. The complexity and smaller scale of the indoor environment impose a great impact on location estimation. The key of location estimation lies in the representation and fusion of uncertain information from multiple sources. The improvement of location estimation is a complicated and comprehensive issue. A lot of research has been done to address this issue. However, existing research typically focuses on certain aspects of the problem and specific methods. This paper reviews mainstream schemes on improving indoor location estimation from multiple levels and perspectives by combining existing works and our own working experiences. Initially, we analyze the error sources of common indoor localization techniques and provide a multilayered conceptual framework of improvement schemes for location estimation. This is followed by a discussion of probabilistic methods for location estimation, including Bayes filters, Kalman filters, extended Kalman filters, sigma-point Kalman filters, particle filters, and hidden Markov models. Then, we investigate the hybrid localization methods, including multimodal fingerprinting, triangulation fusing multiple measurements, combination of wireless positioning with pedestrian dead reckoning (PDR), and cooperative localization. Next, we focus on the location determination approaches that fuse spatial contexts, namely, map matching, landmark fusion, and spatial model-aided methods. Finally, we present the directions for future research.

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An Indoor Collaborative Pedestrian Dead Reckoning System

Cited by 11 publications

References 20 publications

Collaborative Indoor Positioning Systems: A Systematic Review

Collaborative Indoor Positioning Systems: A Systematic Review

Low cost infrastructure free form of indoor positioning

Improvement Schemes for Indoor Mobile Location Estimation: A Survey

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